{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "import os\n",
    "import cv2\n",
    "import h5py\n",
    "from sklearn import preprocessing\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Model, load_model, model_from_json\n",
    "from PIL import Image\n",
    "import imagehash\n",
    "import scipy\n",
    "import csv\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = csv.reader(open('../../../../data/data_set/BIT_label.csv','r',encoding='utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('../../../../data/neural_networks/BIT_VGG16.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = []\n",
    "Y_data = []\n",
    "num_class = 6\n",
    "input_shape=(32,32,3)#3通道图像数据\n",
    "for lists in f:\n",
    "    img = cv2.imread('../../../../data/data_set/BIT/'+lists[1])\n",
    "    img = cv2.resize(img, (input_shape[0], input_shape[1]))\n",
    "    X_data.append(img)\n",
    "    Y_data.append(int(lists[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x,test_x,train_y,test_y = train_test_split(X_data,Y_data,test_size=0.4)\n",
    "train_x = np.array(train_x).astype('float32') / 255.\n",
    "train_y = np.array(train_y) \n",
    "test_x = np.array(test_x).astype('float32') / 255.\n",
    "test_y = np.array(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x_mean = np.mean(train_x, axis=0)\n",
    "test_x_mean = np.mean(test_x, axis=0)\n",
    "train_x -= train_x_mean\n",
    "test_x -= test_x_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = preprocessing.LabelBinarizer().fit(np.array(range(num_class)))  # 对标签进行ont_hot编码\n",
    "train_y = lb.transform(train_y)  # 因为是多分类任务，必须进行编码处理\n",
    "test_y = lb.transform(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_array = [0, 0, 0, 0, 0, 0]\n",
    "num_array = [0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x_perdict = model(test_x)\n",
    "test_x_perdict_arg = np.argmax(test_x_perdict, axis=1)\n",
    "test_y_arg = np.argmax(test_y, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in zip(test_y_arg, test_x_perdict_arg):\n",
    "    num_array[i] = num_array[i] + 1\n",
    "    if i != j:\n",
    "        error_array[i] = error_array[i] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09593908629441625"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(error_array).sum() / np.array(num_array).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate_old = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.20840950639853748,\n",
       " 0.0369244135534318,\n",
       " 0.335243553008596,\n",
       " 0.22388059701492538,\n",
       " 0.05116279069767442,\n",
       " 0.018404907975460124]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i,j in zip(error_array, num_array):\n",
    "    error_rate_old.append(i / j)\n",
    "error_rate_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.79      0.78       547\n",
      "           1       0.94      0.96      0.95      2302\n",
      "           2       0.89      0.66      0.76       349\n",
      "           3       0.78      0.78      0.78       201\n",
      "           4       1.00      0.95      0.97       215\n",
      "           5       0.92      0.98      0.95       326\n",
      "\n",
      "    accuracy                           0.90      3940\n",
      "   macro avg       0.88      0.85      0.86      3940\n",
      "weighted avg       0.90      0.90      0.90      3940\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(test_y_arg,test_x_perdict_arg)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算混淆矩阵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BIT——ResNet生成的杀死率不均衡，查看原始图像和生成测试用例的混淆矩阵发现，有区别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_path = \"../../../../output/grad/VGG16/BIT/generated_samples_d\"\n",
    "deepsmartfuzz_new_inputs = np.load(os.path.join(deepsmartfuzz_path, 'new_inputs.npy'))\n",
    "deepsmartfuzz_origin_inputs = np.load(os.path.join(deepsmartfuzz_path, 'orgin_inputs.npy'))\n",
    "deepsmartfuzz_new_outputs = np.load(os.path.join(deepsmartfuzz_path, 'new_outputs.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_x_mean = np.mean(deepsmartfuzz_new_inputs, axis=0)\n",
    "# deepsmartfuzz_new_inputs -= test_x_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_perdict = model(deepsmartfuzz_new_inputs)\n",
    "deepsmartfuzz_perdict_arg = np.argmax(deepsmartfuzz_perdict, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_new_outputs_arg = np.argmax(deepsmartfuzz_new_outputs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_array = [0, 0, 0, 0, 0, 0]\n",
    "num_array = [0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in zip(deepsmartfuzz_new_outputs_arg, deepsmartfuzz_perdict_arg):\n",
    "    num_array[i] = num_array[i] + 1\n",
    "    if i != j:\n",
    "        error_array[i] = error_array[i] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5758463541666666"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(error_array).sum() / np.array(num_array).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate_new = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8977556109725686,\n",
       " 0.5442064799560681,\n",
       " 0.5017667844522968,\n",
       " 0.5151515151515151,\n",
       " 0.41237113402061853,\n",
       " 0.5311203319502075]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i,j in zip(error_array, num_array):\n",
    "    error_rate_new.append(i / j)\n",
    "error_rate_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(deepsmartfuzz_new_outputs_arg,deepsmartfuzz_perdict_arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.35      0.10      0.16       401\n",
      "           1       0.85      0.46      0.59      1821\n",
      "           2       0.24      0.50      0.33       283\n",
      "           3       0.10      0.48      0.16       132\n",
      "           4       0.18      0.59      0.28       194\n",
      "           5       0.89      0.47      0.61       241\n",
      "\n",
      "    accuracy                           0.42      3072\n",
      "   macro avg       0.44      0.43      0.36      3072\n",
      "weighted avg       0.66      0.42      0.48      3072\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_path = \"../../../../output/grad/VGG16/BIT/generated_samples\"\n",
    "deepsmartfuzz_new_inputs = np.load(os.path.join(deepsmartfuzz_path, 'new_inputs.npy'))\n",
    "deepsmartfuzz_origin_inputs = np.load(os.path.join(deepsmartfuzz_path, 'orgin_inputs.npy'))\n",
    "deepsmartfuzz_new_outputs = np.load(os.path.join(deepsmartfuzz_path, 'new_outputs.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "deepsmartfuzz_perdict = model(deepsmartfuzz_new_inputs)\n",
    "deepsmartfuzz_perdict_arg = np.argmax(deepsmartfuzz_perdict, axis=1)\n",
    "deepsmartfuzz_new_outputs_arg = np.argmax(deepsmartfuzz_new_outputs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(deepsmartfuzz_new_outputs_arg,deepsmartfuzz_perdict_arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.09      0.16       499\n",
      "           1       0.83      0.52      0.64      1782\n",
      "           2       0.27      0.40      0.32       237\n",
      "           3       0.09      0.57      0.15       152\n",
      "           4       0.32      0.54      0.41       162\n",
      "           5       0.44      0.42      0.43       240\n",
      "\n",
      "    accuracy                           0.44      3072\n",
      "   macro avg       0.43      0.43      0.35      3072\n",
      "weighted avg       0.66      0.44      0.48      3072\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7915904936014625,\n",
       " 0.9630755864465682,\n",
       " 0.664756446991404,\n",
       " 0.6397557666214383,\n",
       " 0.14986813713737712,\n",
       " 0.9815950920245399]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for new,old in zip(error_rate_new, error_rate_old):\n",
    "    error_rate.append(new - old)\n",
    "error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_rate_old = [0.77, 0.94, 0.89, 0.78, 1.00, 0.92]\n",
    "precision_rate_new = [0.35, 0.85, 0.24, 0.10, 0.18, 0.89]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.42000000000000004,\n",
       " 0.08999999999999997,\n",
       " 0.65,\n",
       " 0.68,\n",
       " 0.8200000000000001,\n",
       " 0.030000000000000027]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for old,new in zip(precision_rate_old, precision_rate_new):\n",
    "    precision_rate.append(old - new)\n",
    "precision_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_rate_old = [0.79, 0.96, 0.66, 0.78, 0.95, 0.98]\n",
    "recall_rate_new = [0.10, 0.46, 0.50, 0.48, 0.59, 0.47]\n",
    "recall_rate_new_r = [0.9, 0.52, 0.40, 0.57, 0.54, 0.42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.10999999999999999,\n",
       " 0.43999999999999995,\n",
       " 0.26,\n",
       " 0.21000000000000008,\n",
       " 0.4099999999999999,\n",
       " 0.56]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for old,new in zip(recall_rate_old, recall_rate_new_r):\n",
    "    recall_rate.append(old - new)\n",
    "recall_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_rate_old = [0.78, 0.95, 0.76, 0.78, 0.97, 0.95]\n",
    "f1_rate_new = [0.16, 0.59, 0.33, 0.16, 0.28, 0.61]\n",
    "f1_rate_new_r = [0.16, 0.64, 0.32, 0.15, 0.41, 0.43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_rate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.62, 0.30999999999999994, 0.44, 0.63, 0.56, 0.52]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for old,new in zip(f1_rate_old, f1_rate_new):\n",
    "    f1_rate.append(old - new)\n",
    "f1_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画杀死率图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "kill_per_class = [72,72,66,70,65,70]\n",
    "kill_per_class_2 = [72,72,66,70,65,70]\n",
    "kill_origin = [37,52,29,33,32,34]\n",
    "[40,52,48,52,54,51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[35, 20, 37, 37, 33, 36]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kill_rate = []\n",
    "for old,new in zip(kill_per_class, kill_origin):\n",
    "    kill_rate.append(old - new)\n",
    "kill_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kill_origin = np.array(kill_origin)\n",
    "kill_per_class = np.array(kill_per_class)\n",
    "kill_per_class_2 = np.array(kill_per_class_2)\n",
    "kill_rate = np.array(kill_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kill_per_index = np.arange(1, 7)\n",
    "kill_per_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SUV', '轿车', '微型客车', '小型货车', '公共汽车', '卡车']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 公共汽车、微型客车、小型货车、轿车、SUV和卡车。每种车型的车辆数量分别为558辆、883辆、476辆、5922辆、1392辆和822辆。\n",
    "class_name = ['SUV', 'Sedan', 'Microbus', 'Minivan', 'Bus', 'Truck']\n",
    "[1392, 5922, 883, 476, 558, 822]\n",
    "['SUV','轿车','微型客车','小型货车','公共汽车','卡车']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_rate)\n",
    "# plt.plot(data01[\"month\"],data01[\"VALUE\"], c=\"red\",label=\"1948\")\n",
    "# plt.plot(data02[\"month\"],data02[\"VALUE\"], c=\"blue\",label=\"1949\")\n",
    "# plt.plot(data03[\"month\"],data03[\"VALUE\"], c=\"green\",label=\"1950\")\n",
    "# plt.plot(x_axix, train_pn_dis, color='skyblue', label='PN distance')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 75) # y轴范围\n",
    "# plt.title('simple-0.47')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_per_class_2)\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 75) # y轴范围\n",
    "# plt.title('simple-0.92')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class_2')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_rate/75, c=\"red\",label=\"kill_rate\")\n",
    "plt.plot(kill_per_index, recall_rate, c=\"blue\",label=\"recall_rate\")\n",
    "plt.plot(kill_per_index, f1_rate, c=\"green\",label=\"f1_rate\")\n",
    "plt.plot(kill_per_index, precision_rate, c='peru', label='precision_rate')\n",
    "# plt.plot(kill_per_index, recall_rate, c='lime', label='recall_rate')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 1) # y轴范围\n",
    "plt.title('BIT-VGG16')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_rate/75, c=\"red\",label=\"kill_rate\")\n",
    "plt.plot(kill_per_index, recall_rate, c=\"blue\",label=\"recall_rate\")\n",
    "plt.plot(kill_per_index, f1_rate, c=\"green\",label=\"f1_rate\")\n",
    "plt.plot(kill_per_index, precision_rate, c='peru', label='precision_rate')\n",
    "# plt.plot(kill_per_index, recall_rate, c='lime', label='recall_rate')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 1) # y轴范围\n",
    "plt.title('BIT-ResNet50')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kill_per_index, kill_origin/75, c=\"lime\",label=\"kill_rate_origin\")\n",
    "plt.plot(kill_per_index, kill_rate/75, c=\"red\",label=\"kill_rate_add\")\n",
    "plt.plot(kill_per_index, kill_per_class/75, c=\"peru\",label=\"kill_rate_new\")\n",
    "plt.plot(kill_per_index, f1_rate_old, c=\"blue\",label=\"f1_origin\")\n",
    "# plt.plot(kill_per_index, recall_rate, c='lime', label='recall_rate')\n",
    "plt.legend()\n",
    "# plt.xticks(kill_per_index,kill_per_index)\n",
    "plt.xlim(1, 6) # x轴范围\n",
    "plt.ylim(0, 1) # y轴范围\n",
    "plt.title('BIT-ResNet50')\n",
    "plt.xlabel('kill_per_index')\n",
    "plt.ylabel('kill_per_class')\n",
    "plt.xticks(range(1,7,1), class_name)\n",
    "plt.grid(linestyle='--') # figure中的网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.26482044885142486, 0.6120552385657049)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(f1_rate_old,kill_origin)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.08571428571428573, 0.8717434402332361)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(precision_rate_old,kill_origin/75)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6, 0.20799999999999985)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(recall_rate_old,kill_origin/75)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结束的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.029854071701326604, 0.9552321964015311)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(f1_rate_new, kill_per_class)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5002164033860247, 0.3122565813186083)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(precision_rate_new, kill_per_class)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9710083124552245, 0.0012485929031976766)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(recall_rate_new, kill_per_class)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "过程中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.04411764705882353, 0.9338664639731324)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(f1_rate, kill_rate)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.17393131069573456, 0.7417339277316277)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(precision_rate, kill_rate)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5217939320872036, 0.28834323380039373)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation, p = scipy.stats.spearmanr(recall_rate, kill_rate)\n",
    "correlation, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.78, 0.95, 0.76, 0.78, 0.97, 0.95]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_rate_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([35, 20, 37, 37, 33, 36])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kill_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72, 72, 66, 70, 65, 70])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kill_per_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.16, 0.59, 0.33, 0.16, 0.28, 0.61]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_rate_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.62, 0.36, 0.43, 0.62, 0.69, 0.33999999999999997]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5099999999999999"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(f1_rate).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f1 in recall_rate:\n",
    "    total = total + math.pow((f1 - np.array(recall_rate).mean()), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27695"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "grad",
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   "name": "grad"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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